Transcriptome Profiling of Human and Murine ESCs Identifies Divergent Paths Requ - 《stemcells》2004-2005文献干细胞之家



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发表于 2009-3-5 10:52 |只看该作者 |倒序浏览 |打印

a Genome Institute of Singapore, Singapore;: Z  }. k0 ~, v; z
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b National Institute of Ageing; Stem Cell, Laboratory of Neuroscience, Baltimore, Maryland, USA;7 N/ g& u+ g& y* _% |; R4 C
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c Division of Cancer Biology, Beth Israel Deaconess Medical Center, Harvard Institutes of Medicine, Boston, Massachusetts, USA;
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d Department of Biological Sciences, National University of Singapore, Singapore;
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e Lynx Therapeutics Inc., Hayward, California, USA;
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f Embryo Stem Cell International, Singapore;
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g Geron Corporation, Menlo Park, California, USA

Key Words. Embryonic stem cells, murine and human ? Transcriptome ? Massively parallel signature sequencing (MPSS)
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Correspondence: Bing Lim, M.D., Ph.D., Genome Institute of Singapore, 60 Biopolis Street, Genome#02-01, Singapore 138672. Telephone: 65-6478-8000; Fax: 65-6478-9005; e-mail: limb1@gis.a-star.edu.sg; and Mahendra Rao, M.D., Ph.D., National Institute of Ageing: Stem Cell, LNS, GRC, 333 Cassell Drive, Baltimore, MD 21224. Telephone: 410-558-8204; Fax: 410-558-8249; e-mail: raomah@grc.nia.nih.gov2 ]2 S$ P1 X- P" c6 u7 l

ABSTRACT' z5 Z( j- u. a3 C" R
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Human embryonic stem cells (hESCs) are a versatile and valuable source of tissue-specific stem cells in regenerative medicine, and the ability to manipulate their growth and differentiation is a major challenge. Many of their molecular characteristics are still poorly understood. All stem cells share properties of pluripotency and self-renewal capacity that are progressively restricted when stem cells undergo differentiation. Progress toward the molecular understanding of these properties has been made, with detailed work on candidate genes for these developmental decisions. An important complementary study is the comprehensive elucidation of the genetic components and programs regulating stem cell fate decisions. To this end, several groups have begun the analysis of the transcriptome of hESCs using the generation of expressed sequence tags (ESTs), serial analysis of gene expression (SAGE), microarray, and massively parallel signature sequencing (MPSS) .! x. g8 T6 j( ]% I

ESCs can be propagated as undifferentiated cells in large numbers more easily than can adult stem cells. ESCs are excellent tools for studying early events in development as the generation of ESC-derived embryoid bodies (EBs) recapitulates early embryo development. ESCs have been isolated from multiple species, including murine, swine, simian, and human blastocysts. Mouse and human ESCs are similar in that they grow as colonies of tightly packed cells on inactivated murine embryonic fibroblast (MEF) feeders or in conditioned medium (CM) derived from such MEFs . Both stem cell populations have the potential to form teratomas and to differentiate in vitro into all three germ layers〞namely, ectoderm, endoderm, and mesoderm. Many markers characteristic of undifferentiated cells, including oct-4, nanog, sox-2, and utf-1, are expressed by both populations of cells. The expression of these markers, together with the absence of differentiation markers, constitutes a signature profile of undifferentiated ESC cultures irrespective of their species origin .

Nevertheless, important differences exist in the growth rates, culture requirements, and marker expression of human and murine ESCs. This divergence has generally been ascribed to fundamental differences in the pathways that regulate self-renewal, apoptosis, and proliferation . Some examples include SSEA1, SSEA3, and SSEA4 expression ; the ability to differentiate into trophoblasts ; and the dependency on leukemia inhibitory factor (LIF) . These multiple reported differences raise the possibility that additional differences exist and provide a compelling rationale to comprehensively map the transcriptome of ESCs.

Human and mouse ESC transcriptomes have been individually mapped to varying depths and breadths with regard to their respective genomes , though no detailed pairwise comparisons have been performed. Sato et al.  compared human and murine ESCs using microarray. More recently, Ginis and colleagues  compared the expression of about 400 genes in human and murine ESCs and showed that at least a quarter of the genes tested have significant differences in their expression. These results suggested that the differences represent, at best, a small fraction of the variations that exist and that a large-scale analysis would identify more important differences.0 U  q) ?4 \% [* J( J
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Comparisons reported so far have been limited by the availability of cross-species and homologous arrays. Other large-scale techniques, such as SAGE and the generation of ESTs, have not been used, perhaps because of the cost and the limitations in gene annotation. More recently, better annotation of genomic data and improvements in technology, together with development of alternative techniques such as MPSS , have permitted a deeper and more complete mapping of transcriptomes at a significantly cheaper price than with conventional SAGE and EST generation. The underlying principle of MPSS, like SAGE, is that a signature sequence of 20 bases starting from the 3'-most DpnII (GATC) site is generated for each transcript. In MPSS, at least 1 million 20-base tags are identified. Considering that an estimated 200,000–300,000 transcripts exist in a single cell, this method theoretically allows for all transcripts in a cell to be measured without the constraints of probe availability. It provides an unprecedented coverage in depth and breadth of any transcriptome at a greatly enhanced sensitivity compared with the average SAGE library tag generation, which is typically tens of thousands of tags. MPSS analysis measures transcript levels using a standard unit of measurement, transcripts per million (tpm), rather than a relative unit in reference to a biological RNA standard, thus allowing one to estimate the copy numbers of different transcripts per cell and compare expression patterns across homologous cells of different species. Furthermore, MPSS has the ability to detect novel transcripts.0 g. w7 T) L+ T. l8 `; V' C% m
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We have chosen MPSS to examine gene expression in ESCs from the murine E14 line and in independently derived hES Clines:The HES-2 line is from ES Cell International(Singapore, http://www.escellinternational.com), and the pooled cell lines H1, H7, and H9 are from WiCell Research Institute, Inc.(Madison, WI, http://www.wicell.org). MPSS has allowed us to assess the complexity of ESCs and EBs and to generate a far more exhaustive list of differences and similarities between mouse and human ESCs than has previously been reported. By comparing gene expression between hESC lines, we also identified culture, developmental, and allelic differences. The usefulness and power of developing such a comprehensive expression database is highlighted by our ability not only to identify putative signaling or biochemical pathways active in ESCs but also to assess the integrity of these pathways from receptors to signaling intermediates and then target substrates at the transcript level.# y; L( j( x. w$ g  `" g

MATERIALS AND METHODS& Q/ c# H4 P. V
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Evaluation of Transcriptome Complexity in ESCs by MPSS Analysis

Two MPSS datasets were generated from RNAs of undifferentiated feeder-free mESCs and day-4 mEBs. Three MPSS datasets were generated from RNA of hESCs, two representing undifferentiated hESCsESI and hESCsWi and one from 12-day hEBsWi. The hESCWi RNA was from a pool of three National Institutes of Health (NIH)–approved lines (H1, H7, and H9) from WiCell, grown feeder-free , and hEBWi RNA was derived from EBs from these three cell lines. The hESCESI line was grown in the presence of MEF feeders.

RNA samples that passed through quality-control checks were subjected to MPSS analysis (see Materials and Methods). Signature sequence tags of 20 bp in length were generated to a depth of greater than 2.2 million tags for each sample. Tag counts of each unique signature were expressed as tpm. From the MPSS libraries, the total number of tags successfully sequenced from four different runs was 2,660,962; 2,367,247; 2,295,140; 2,403,315; and 2,591,008 for mESCs, mEBs, hESCsESI, hESCsWi, and hEBsW, respectively. Distinct signatures present in at least two MPSS runs and presented as at least 4 tpm per run totaled 13,824; 9,845; 20,027; 23,500; and 17,278 for mESCs, mEBs, hESCsESI, hESCsWi, and hEBsWi, respectively.  ^% G+ N4 e; Q
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We first evaluated the total complexity of the signature generated for each sample, expressed as total significant signatures with the cumulative tpm (with cutoff at >10 tpm) of signature distributions for mESCs, mEBs, hESCsESI, hESCsWi, and hEBsWi , as shown in Table 1 (for complete data, see supplementary online data 2 for hESCs and hEBs and supplementary online data 3 for murine equivalents). The distributions of the number of unique signature tags and their percentages are compiled cumulatively from the highest abundant signatures (0.04%–0.20%) to the lowest abundant signatures (total 55%–70%).

Table 1. Distribution of genes with expression levels from >10,000 tpm to >10 tpm in murine E14 embryonic stem cells (mESCs); day-4 murine embryoid bodies (mEBs); human HES-2 (hESs ESI); pooled human ESC lines H1, H7, and H9 (hESCsWi); and human day-12 EBs derived from hESCsWi
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Despite the difference in transcriptome complexity, the distribution of signature sequences based on abundance was strikingly similar in human and mouse ESCs. Fewer than 2% of the signature sequences were expressed at the high level of greater than 1,000 tpm; more than 70% of signatures were expressed at less than 50 tpm; and more than 30% of all signatures were present at a level of 10 tpm or lower (Table 1 and supplementary online Table 1). The typical detection limit by microarray analysis and SAGE is estimated at around 55 tpm .
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The unique signature sequences were then mapped to Unigene clusters (Mm.130 and Hs.163), resulting in 6,712; 5,779; 9,093; 9,953; and 8,950 unique Unigene IDs identified in mESCs, mEBs, hESCsESI, hESCsWi, and hEBsWi, respectively (Table 1; see supplementary online Table 1 and accompanying text for explanation). The reduced number of Unigene signatures resulted from multiple signature sequences mapping to the same Unigene cluster. Thus, the complexity of ESCs is comparable to that seen in somatic cell populations examined by MPSS . Interestingly, for both the mouse and human samples, the undifferentiated cells had a slightly higher level of complexity than the corresponding EBs had. Examination of the most abundant genes showed that, to a large extent, the top 200 or so genes were comprised of ribosomal, mitochondrial, and housekeeping genes, while growth factors, transcription factors, and regulators of gene expression were expressed in the low tpm range. The low abundance of regulatory and biologically relevant genes highlighted the importance of analyzing expression at high resolution by methods such as MPSS.
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MPSS Provides a Robust Assessment of the ESC State4 i( w- _! L& x5 X4 n9 B5 n( k$ w
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Theoretically, MPSS at greater than 2.2 million signatures generated per sample should provide relatively comprehensive coverage of gene expression in a given cell type. To directly ascertain the quality of data, we first examined a list of ESC-specific genes that are known to be expressed at moderate abundance in murine and human ESCs. As shown in Table 2, genes such as oct-4/pou5f1, sox-2, utf-1, and tdgf-1 were well represented in MPSS-derived transcriptome maps of both human and murine ES lines. Markers of differentiation known to be upregulated in differentiating EBs (e.g., COL4A2  and AFP ) showed the expected increase in transcript frequency as ESCs differentiated.
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Table 2. Expression frequency (tpm) of selected ESC–specific genes and differentiation markers (COL4A2  and AFP ), along with housekeeping markers (GAPD and ACTB )/ j) d/ u: n4 E8 ~9 f

As a further test of general robustness, the MPSS data were compared against a list of 283 "ES-specific" genes derived from the intersection of three independent studies comparing murine ESCs with various differentiated cells by microarray analysis aimed at identifying genes preferentially expressed in mESCs . Table 3 shows a list of selected examples of these genes and the corresponding MPSS tpm values from mouse and human ESC and EB datasets and the average rank of each gene in the three microarray datasets (complete dataset in supplementary online data 4).& p& M" J8 e/ l) E  |
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Table 3. Comparison of ESC–enriched gene expression in human and murine ESCs

This initial assessment of MPSS analysis allowed us to make several pertinent observations about the interpretations and usefulness of MPSS-generated datasets. The first observation was that almost all the genes detected by micro-array can be detected in mESCs by MPSS. While there was good correlation between transcript presence or absence as measured by MPSS and by microarray chip analysis, there was not a highly predictable correlation between transcript levels estimated by these two methods. These differences may be due to compression of signal intensities that is often observed in microarrays.
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The second observation was that some genes detected by microarrays were not detected by MPSS. This arose from some technical limitations of the MPSS technology that include failure to identify cDNAs lacking a DpnII site (e.g., murine trap1a, Table 3), cDNAs containing a double palindrome within the tag (preventing sequencing by MPSS), and cDNAs with the respective tag falling in a repeat region (e.g., human nanog and rex-1). For these reasons, it is important to know the tag status of the genes of interest before concluding that it is not expressed based on the MPSS data alone. From EST data, trap1a is known to be expressed in mESCs, and likewise with nanog and rex-1. For all subsequent analysis of genes presented in the remaining figures, we took into consideration the technical limitations of MPSS data before calling a tag count as zero.

A third observation was that mouse and human ESCs appeared to differ in fundamental ways based on differing expression levels between homologous genes identified from our analysis. Many of the differences could be verified by RT-PCR (see next section), suggesting that MPSS can be used for cross-species comparisons. Thus, while MPSS was unable to detect a small fraction of genes, this methodology appeared sensitive and reliable. Additional expression data, not described here, further confirmed that MPSS analysis accurately and robustly described the transcriptome of human and murine ESCs and revealed true differences between the species.

Global Comparison of Mouse and Human ESC Transcriptome. h7 h6 ^- U3 T6 J- B$ ?
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To capture an overall impression of the similarities and differences between the ESCs, we compared the transcriptome of human and murine ESCs on a global scale in which homologues that could be reliably identified were compared in a pairwise manner and displayed in dot plots (Fig. 1). Based on 5,921 identified homologous genes, transcriptomes of mouse cells (mESCs) and human cells (hESCsESI) were significantly different, with a poor correlation coefficient of .41 (Fig. 1B). This degree of correlation is less than correlation typically observed between different lineages of the same species compared using microarray profiling. The coefficient was also lower than that between ESCs and their differentiated derivative EBs: .82 for mouse (Fig. 1C) and .49 for human (Fig. 1D). The discrepancy between the ESC/EB correlation coefficients was most likely a result of the difference in the length of time allowed for EB formation: 4 days for murine and 12 days for human cells. Hence, murine day-4 EBs were less differentiated than human day-12 EBs.( J( a& e7 K7 O8 j4 `3 j% x
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Figure 1. Global comparison of hESC and mESC transcripts. Scatter plots for murine and human homologous genes comparing (A) hESCsWi with hESCsESI (10,084 data points; at least one tpm per Unigene Hs.163 cluster found in either sample); (B) hESCsESI with mESCs (5,921 data points; one tpm per mouse Mm.130 or human Hs.163 for which homology is known, found in either mouse or human); (C) mESCs with mEBs (6,889 data points; one tpm per Unigene Mm.130 cluster found in at least either ESCs or EBs); (D) hESCsWi with hEBsWi (10,182 data points; one tpm per Hs.163 found in either ESCs or EBs). All scatter plots were drawn after the removal of ribosomal proteins (with mitochondrial genes filtered out in the original lists). The corresponding correlation coefficients are shown in the panels. Abbreviations: EB, embryoid body; hESC, human embryonic stem cell; mESC, murine embryonic stem cell.6 v9 V7 R4 n, s- ^$ _0 u
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Thus, despite the overall similarity in their self-renewal capacity, the expression of some ESC markers and their pluripotential capabilities, human and murine ESCs differ significantly from each other on a global scale. The low correlation is unlikely to be attributable to major technical issues, as the two independently derived and maintained hESC populations (hESCsWi and hESCsESI) showed a very high degree of correlation: .90 (Fig. 1A). Overall, these results confirmed that there are fundamental differences in the transcriptomes of human and mouse ESCs that cannot be attributed to differences in annotation and species-specific differences in MPSS analysis.
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The differences between murine and human transcript levels ranged from one- or twofold to over 50-fold, and even genes known to be important for ESC self-renewal varied by as much as six- to sevenfold. For example, the oct-4 level was 2,173 tpm (hESCsESI) or 658 tpm (hESCsWi) in hESCs and 388 tpm in mESCs. Therefore, by a global pairwise comparison, we developed sublists of genes that varied between human and murine ESCs by 5-fold, 10-fold, or 50-fold and were expressed at 50 tpm or higher (Table 4 and supplementary online data 5, 6, and 7). At the least stringency (>fivefold difference, tpm >50-fold if the other species’ corresponding tpm is zero), we found 1,153 genes higher in human than murine ESCs (supplementary online data 5A) and 427 genes higher in murine than human ESCs (supplementary online data 5B). At the highest stringency (>50-fold differences, tpm >250 if the other species’ tpm is zero), 101 genes were found to be higher in human ESCs (supplementary online data 7A) and 64 in murine ESCs (supplementary online data 7B).

Table 4. Summary of global comparison of human and mouse embryonic stem cell (ESC) gene expression profiles

The differences noted between human and murine ESCs cannot be discussed in complete detail, and readers can examine and use the supplementary online data (5, 6, and 7) for detailed information.
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Genes differentially expressed were functionally categorized (by the gene ontology classification) to determine if differences were restricted to particular classes.* h2 X. E. B" o! _# Y4 b( z
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Data depicting the global comparison of the genes showing greater than fivefold difference between species was plotted as a pie chart (Fig. 2). As can be seen, human and murine ESCs differ from each other in a wide spectrum of genes, with the largest difference being due to "unknown" genes (22%).1 `0 c1 H0 r( ~. d9 |" r% Q
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Figure 2. Global comparison between the species based on the least stringent criteria of >10-fold differences. The categories of genes as derived by the gene ontology classification of genes are shown with the respective percentage distribution. The hES and mES cells differ in a wide spectrum of genes, with the largest cause of the difference being due to "unknown" genes (22%). Abbreviations: hES, human embryonic; mES, murine embryonic.% ]: S* h! r% d+ u, C

A subset of genes that differ between species, as defined by MPSS readings, are shown in Table 5, with further verification by RT-PCR of selected genes shown in Figure 3B., m7 d' d* m$ d  a1 @$ P6 |

Table 5. Embryonic stem cell (ESC) genes differentially expressed between species1 \+ R2 J% n% X, {- e3 C

Figure 3. Difference between hESCs and mESCs. Analaysis by RT-PCR was done to validate the massively parallel signature sequencing demonstration of differences between species. Total RNA was isolated from hESCs and mESCs, and the total RNA was reverse transcribed in the presence of oligo-dT. PCR was then performed by using gene-specific primers. The PCR products were electrophoresed in 2.0% agarose ethidium bromide gels. To confirm the quantity of reverse-transcribed cDNA in hESCs and mESCs, semiquantitative RT-PCR was performed using 2- to 16-fold dilutions of each first-strand cDNA reaction mix with primers for G3PDH at 28 cycles. (A): The quantity of RT products in hESCs and mESCs was equivalent. G3PDH was used as an internal control. (B): Examples of genes differentially expressed between human and murine ESCs. Abbreviations: hESC, human embryonic stem cell; mESC, mouse embryonic stem cell; RT-PCT, reverse transcription polymerase chain reaction.0 p# S$ `' C: e7 ^& n

Using MPSS to Identify Distinctive Molecular or Biochemical Pathways
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Despite having many common characteristics, the large number of differences observed between murine and human ESCs suggests that species-specific transcripts are likely to define biological pathways that distinguish murine and human ESCs. These pathways would include genes for cytokine and signal transduction, membrane protein, and structural and matrix genes. A full list of the differences is provided in the supplementary online information, but we have highlighted some pathways here. To assess the molecular basis of growth differences between human and murine cells, we queried the MPSS databases for >50-fold differences between them. In the 63 genes of murine > human category, 5/63 of the genes (highlighted in supplementary online data 7B) were directly involved in generating ATP by oxidative phosphorylation. This suggests that mESCs have a greater capacity to generate ATP and have a higher metabolic activity powered by mitochondrial oxidation. Consistent with their higher metabolic activity, mESCs have more GLUT1/SLC2A1 transcripts than hESCs have, while hESCs have more GLUT8/SLC2A8 transcripts (Table 5). GLUT1 maintains basal glucose uptake for metabolism in many cell types, including oocytes, and many stages of embryonic development through the blastocyst stage  and haploinsufficiency of GLUT1 results in deficient glucose transport . However, GLUT8 is an insulin-regulated glucose transporter that translocates from an intracellular pool to the plasma membrane upon insulin stimulation . Therefore, the differential transcript levels of GLUT1 and GLUT8 in human and mouse ESCs suggest that glucose uptake is more efficient and less insulin-dependent in mESCs and provides a biochemical basis for their higher level of oxidative phosphorylation. In contrast, glucose uptake in hESCs is likely to be insulin-dependent, and this may be the underlying biochemical basis for the need to optimize hESC culture media but not mESC media with insulin supplement.

To examine further the ability of MPSS to derive biologically relevant insights from transcriptomes, we selected four specific signaling pathways that have prominent roles in the growth and development of ESCs: LIF, gp130, FGF, Wnt, and transforming growth factor–beta (TGF-?) pathways (Tables 6 and 7, Fig. 4).0 M9 r+ |2 S4 N( r7 {
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Table 6. Comparison of leukemia inhibitory factor (LIF), fiberblast growth factor (FGF), and Wnt pathway genes in human and murine embryonic stem cells (hESCs and mESCs, respectively) (see Fig. 4)
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Table 7. Transforming growth factor–beta (TGF-?) signaling pathway in murine and human embryonic stem cells (mESCs and hESCs, respectively)( s+ n; i2 {6 b* {2 q* ?/ ?
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Figure 4. Cross-species comparison of the expression levels (tpm) of genes from key signaling pathways and the reverse transcription polymerase chain reaction validation of massively parallel signature sequencing readings. (A): LIF and LIF transducers. (B): FGF and FGF receptors. (C): Wnt/?-catenin pathway. Abbreviations: hES, human embryonic stem; mES, mouse embryonic stem.

LIF and LIF Transducers

The propagation of mESCs depends on the presence of LIF to engage a heterodimeric cytokine receptor complex consisting of gp190 LIF-specific receptor chain (LIFR) and the gp130 chain, a common component of various cytokine receptors. The LIFR complex activates Janus-associated tyrosine kinases (JAK), which then phosphorylate the signal transducer and activator of transcription (STAT) . Unlike mESCs, hESCs are strikingly unresponsive to LIF-mediated proliferation and maintenance of the undifferentiated state. An explanation for this difference is provided by the MPSS data, which showed that murine, but not human, ESCs express LIFR transcripts (Table 6) together with significant levels of JAK and STAT3. Transcripts for gp130 were absent in hESCs, although upon differentiation, human EBs expressed LIFR (32 tpm, supplementary online data). The absence of LIFR and JAK in hESCs, along with higher levels of SOCS genes (which inhibit LIF-mediated signaling), is consistent with the failure of LIF to support hESC self-renewal and suggests that other members of the LIF/interleukin-6 signaling family cannot substitute for LIF. However, the presence of STAT3 in hESCs, though significantly lower than in mESCs by MPSS, raises the possibility of recruitment and activation of STAT3 by an alternate LIF-independent pathway. Intriguingly, MPSS indicated that mESCs had no or low gp130 transcripts. This low level was supported by the lack of mESC-derived gp130 ESTs in the public databases (see Mm.250251). However, RT-PCR analysis revealed that besides LIFR, transcripts for gp130 and Stat3 were easily detected in mESCs (Fig. 4A).( f6 y; b5 N5 }# v1 O$ Z

FGF and FGF Receptors

Basic FGF (FGF2) is currently used for the propagation of hESCs , suggesting a requirement for Fgf signaling in the maintenance of pluripotency in these cells. Culture media for mESCs is not supplemented with any of the 22 known FGFs. Furthermore, mESCs (and the inner cell mass) are known to synthesize FGF4, which is required for paracrine signaling to the trophectoderm and the primitive endoderm for normal development to continue beyond the peri-implantation stage of development . For these reasons, we compared the expression of molecules involved in the FGF signaling pathway. Clearly, hESCs are poised to respond to FGF signals, with three of the four FGF receptors (FGFR-1 ,-3, and -4) having substantial levels of expression (Table 6). In addition, frs2, one of the major downstream effectors of FGF receptor signaling, was detected by MPSS in hESCs. In contrast, mESCs contain a minimal level of fgfr1 (14 tpm) and zero tag counts for the other three FGF receptors and frs2. Curiously, hESCs express significant levels of FGF2 transcripts, whereas FGF2 was undetectable in mESCs.

As expected, FGF4 was found at significant levels in mESCs but was apparently absent in hESCs. Both the FGF2 and FGF4 MPSS data were confirmed by RT-PCR (Fig. 4B).
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Wnt/?-Catenin Network0 p3 |8 _  H, g  A  f

The Wnt-signaling pathways mediate important decisions between proliferative self-renewal and differentiation . Recently, Sato et al.  have suggested that the canonical GSK3/?-catenin pathway may be active in undifferentiated cells and inhibition of glycogen synthase kinase-3 (gsk-3) was sufficient to maintain the undifferentiated phenotype in both murine and human ESCs. Comparison of pathway gene expression confirmed that most of the components in the canonical Wnt/?-catenin signaling pathway were present in both cell types (Table 6). In hESCs, RT-PCR (Fig. 4C) generally confirmed the presence of the key components, as predicted by MPSS (Table 6). However, in mESCs, the low level of transcripts for most of the components of the canonical Wnt/?-catenin signaling pathway〞including the absence of some key molecules〞suggested that this pathway may not be active. MPSS readings, confirmed by RT-PCR, showed that APC , while low in hESCs, was not detected in mESCs. EST data also supported this finding. The low tpm readings for lrp5 and lrp6  (Table 6) were confirmed by our negative RT-PCR result (Fig. 4C). In contrast, these components were present in human cells both by RT-PCR/MPSS and EST data (data not shown). The presence of an intact or complete Wnt-signaling pathway with all the attendant positive and negative regulators was further evidenced by the high expression of frizzled-related proteins (FRPs) in human but not mESCs (Table 6). FRPs are known to antagonize Wnt signaling . Therefore, hESCs appeared better poised than mESCs to engage the Wnt-signaling pathway.

TGF-? Superfamily
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The TGF-?/bone morphogenic protein (BMP) family has been shown to play important and pleiotropic parts in early development and in regulating self-renewal of somatic stem cells . It has also been shown that a combination of BMP4 and LIF can support propagation of mESCs in a serum-free condition . We examined the expression of both the TGF-?/activin/nodal subfamily and the BMPs, along with their receptors and modulators, and the downstream Smads that they activate . As shown in Table 7, the absence of all the receptor-associated Smads (Smad-1 ,-3 ,-5, and -8) in mESCs suggests that any TGF- ?/BMP signaling in mESCs would likely be through a Smad-independent route. The presence of these receptor Smads in hESCs, as detected by MPSS, suggests that the Smad-mediated TGF-? pathway is functionally important to hESCs. Furthermore, there are distinctive differences in the ID, BMP, and activin receptor genes between the species. A TGF-?–focused chip containing probes for a spectrum of the TGF-?/BMP superfamily was compared with the MPSS data. The presence ( ) or absence (–) of hybridization signals in the chip, as shown in Table 8, indicated a good concordance between the MPSS and the array results; however, the MPSS approach was more quantitative and sensitive. Overall, the results showed that significant differences exist between the species and that Smad-dependent TGF-?/BMP signaling appeared to be much more actively recruited in hESCs than in mESCs., p, n. F( b9 O5 g' E: u

Table 8. Genes with expression pattern conserved between murine and human embryonic stem cells (mESCs and hESCs, respectively)- r. ^8 n/ E% h8 u' C% b+ p

Similarities between Murine and Human ESCs

While we have highlighted differences between murine and human ESCs, we noted that similarities exist as well. In particular, some well-known genes thought to be involved or related to ESC self-renewal pathways were conserved (Table 2 and supplementary online data). These included expression of oct-4, sox-2, bmpr, nodal, lefty, tert, and cripto. We reasoned that if genes were coexpressed in both species, despite the overall low concordance, then this subset would likely be enriched for genes important in the self-renewal process of both species. If this was further limited to genes that were downregulated as EB differentiation occurred, then the specificity would be higher. Therefore, we identified genes that were expressed in both mouse and human undifferentiated ESCs and were low or downregulated upon differentiating into EBs. Three separate lists were generated based on levels of expression. List 1 (607 genes, supplementary online data 8) shows all the genes with an ESC/EB ratio of two-fold or higher, and an ESC 50 tpm in both species if EB is zero. The 16 genes in List 3 are shown in Table 8. As expected, known genes that are ESC-specific were identified (oct-4, leftB). Other known ESC-specific genes (see supplementary online data) fell within the 2- to 10-fold range such as dnmt31, utf-1, sox-2 , tdgf, and dppa2. Several additional genes not previously known to be conserved and elevated in ESCs were identified as well. Of particular interest was lin-28, a heterochronic gene known to be important in regulating the appropriate timing of differentiation . Another gene, mortality factor 4 (morf412), is a member of a novel family of genes with transcription-like motifs that induces a senescent-like phenotype in immortal cell lines . SUMO-specific protease 3 (senp3) is a member of a novel class of regulators of Sentrin/SUMO (small-ubiquitin-like modifiers) . CCCTC-binding factor (ctcf) is a ubiquitous zinc finger (ZF) protein that is not only involved in transcriptional silencing or activating in a context-dependent fashion but also organizes epigenetically controlled chromatin insulators that regulate imprinted genes in soma .' Q0 M; e; V+ ~# x
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Comparison of hESC Lines

The high similarity between hESCWi lines grown feeder-free and the hESCsESI grown on MEFs suggested that, overall, different hESC lines are similar and the differences observed between murine and human cells must represent fundamental species-specific differences. However, differences between human lines likely exist. We and others have noted some differences between ESC lines , although no comprehensive comparison has been performed. We therefore examined the MPSS dataset to identify genes that showed a 10-fold or higher difference between human samples. A complete list is provided in supplementary online data 11A for hESCWi > hESCESI and 11B for hESCESI > hESCWi. Overall, even at a stringent criteria of 10-fold and higher, over 1,000 genes were highly expressed in hESCsESI and absent in hESCsWi. Several of these genes were shared by murine and human cells but not by the two ESC populations tested (supplementary online data 11). Figure 5 and Table 9 show a selected list of genes and a confirmation of the differential expression of a subset of these genes by semiquantitative RT-PCR. For example, differences in expression of collagen and BMP-related genes were seen. These were likely due to the difference in culture conditions, while other differences〞such as those in FoxD3〞represent allelic differences. Other differences noted include matrix proteins, junction proteins such as claudin, insulin-like growth factor binding proteins, and several novel genes of unknown function.) O8 v/ ^; [8 G7 s
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Figure 5. Differences between hESC lines. MPSS of hESCsESI and hESCsWi was examined for differential expression of genes at >10-, >50-, and >100-fold differences between the two ESC lines. Shown here are examples of genes expressed at markedly different levels (tpm) between the two cell lines. Ethidium bromide gel analysis of selected examples of genes from Table 9 (marked in bold) showed the concordance of the RT-PCR results with MPSS. Additional genes that are discussed in the text but are not in the table (rex-1, lif-R, fgf4) were included in the RT-PCR analysis. Abbreviations: hESC, human embryonic stem cell; MPSS, massively parallel signature sequencing; RT-PCR, reverse transcription polymerase chain reaction.7 q3 n+ L9 u9 h' @

Table 9. Differences between hESCESI and hESCWi lines$ E( M5 t1 T% J$ p; C" O# D
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Some of these differences may be attributed to differentiated cell types known to exist, though at a relatively low level, in hESCs grown under feeder-free conditions . Another source of sequence tag difference between the two hESC lines is from mouse cells contaminating the hESCESI line that was grown on mouse feeders, MEFs. We estimated that this contamination occurs at a frequency of approximately 0.3% (see supplementary online Table 2 for data and explanation of computation).
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Overall, our results showed that MPSS is sensitive and versatile in successfully identifying multiple differences and similarities between and within species; it can be used to obtain a unique profile of each individual cell line. The results also indicate that murine and human ESCs differ fundamentally in the network of genes that are conscripted to confer their apparently similar cellular properties of totipotency and high self-renewal capacity.4 X, \' ]9 j! ~$ L- S) S7 n

DISCUSSION8 y  d9 E7 V% B' h5 W6 r
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This study was supported by A-Star (Singapore) and grants from NIH DK47636 (B.L.) and NIH (M.R.). We thank M. Bakre, Nicolas O. Fortunel, Huck Hui Ng, Leonard Lipovich, and Janet Buhlman for reading and helpful discussions of the manuscript.
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